A Framework for the Dissemination of Hydrological Models for Non-Expert Users1
Timo Schaffhauser
a
, Daniel Garijo
b,g
, Maximiliano Osorio
b
, Daniel Bittner
c,f
, Suzanne Pierce
d
,2
Hernán Vargas
b
, Markus Disse
a
and Yolanda Gil
b,e
3
a
School of Engineering and Design, Technical University of Munich, Munich, Germany4
b
Information Sciences Institute, University of Southern California, Marina del Rey, California, United States5
c
Erftverband, Department for River Basin Management, Bergheim, Germany6
d
Texas Advanced Computing Center and Jackson School of Geosciences, The University of Texas, Austin, Texas, United States7
e
Department of Computer Science, University of Southern California, Los Angeles, California, United States8
f
Institute of Groundwater Management (IGW), Technical University of Dresden, Dresden, Germany9
g
Ontology Engineering Group, Universidad Politécnica de Madrid, Madrid, Spain10
11
A R T I C L E I N F O
Keywords:
software metadata
model metadata
model encapsulation
model catalogs
MINT
hydrological models
12
A B S T R A C T13
14
Hydrological models are essential in water resources management, but the expertise required to15
operate them often exceeds that of potential stakeholders. We present an approach that facili-16
tates the dissemination of hydrological models, and its implementation in the Model INTegra-17
tion (MINT) framework. Our approach follows principles from software engineering to create18
software components that reveal only selected functionality of models which is of interest to19
users while abstracting from implementation complexity, and to generate metadata for the model20
components. This methodology makes the models more findable, accessible, interoperable, and21
reusable in support of FAIR principles. We showcase our methodology and its implementation in22
MINT using two case studies. We illustrate how the models SWAT and MODFLOW are turned23
into software components by hydrology experts, and how users without hydrology expertise can24
find, adapt, and execute them. The two models differ in terms of represented processes and in25
model design and structure. Our approach also benefits expert modelers, by simplifying model26
sharing and the execution of model ensembles. MINT is a general modeling framework that uses27
artificial intelligence techniques to assist users, and is released as open-source software.28
29
ORCID(s):
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Highlights30
An approach that facilitates hydrological model dissemination from expert modelers to non-experts31
Software engineering methods are proposed to simplify model complexity by creating software components32
Non-experts can easily modify selected parameters and execute models provided by experts33
Our approach makes models more findable, accessible, interoperable, and reusable in support of FAIR principles34
Various applications benefited from this approach within the MINT framework35
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1. Introduction36
Hydrological models (HMs) are commonly used for water resources management and are mainly developed and37
used by expert researchers or engineers working in the water sector. The results of HMs are important and considered38
in decision-making processes of government agencies (Ruiz-Ortiz et al., 2019; Andreu et al., 1996). HM applications39
include estimation of water availability (Döll et al., 2003), development of water management strategies (Haasnoot40
et al., 2011), flood risk assessment (Merz et al., 2010), climate impact analysis (Krysanova and Hattermann, 2017;41
Lobanova et al., 2018; Hattermann et al., 2018), solute transport (Konikow, 2010; Morales et al., 2010) and spatial42
characterization of hydrological system variables such as soil water content (Brocca et al., 2017), desalination and43
industrial wastewater treatment (Panagopoulos, 2022) as well as groundwater heads (Reinecke et al., 2019). HMs44
vary widely in terms of their mathematical description of prevalent hydrological processes and their spatial model45
structure, ranging from lumped conceptual models (Bittner et al., 2018; Booij and Krol, 2010) to distributed physical46
models (Brunner and Simmons, 2012; Newman et al., 2017).47
A fundamental understanding of hydrological processes is needed in order to reasonably set up a hydrological model48
for a new region or modeling problem. This may become an obstacle for the use of HMs by decision-makers and other49
users (Lüke and Hack, 2018). In practice, model results are presented to decision-makers as a summary focusing only50
on a few specific variables of interest, such as streamflow or groundwater heads. The interests and requirements of51
decision-makers and various stakeholders can diverge widely from what may be hydrologically interesting. Decision52
makers in water resources management are usually interested in the assessment of the water balance, primarily the53
availability of water in space and time. HMs allow a holistic view on the components of the water cycle, from which54
insightful information, e.g. limiting factors in space and/or time, can be derived. These variables do not necessarily be55
restricted to water availability, but could also refer to evapotranspiration, soil water or precipitation. Miscommunication56
between science and non-expert groups is therefore not a rarity (Timmerman and Langaas, 2005). This increases the57
“science-policy gap" due to differences in the level of knowledge between the information producer and receiver58
(Bernstein et al., 1993; Bradshaw and Borchers, 2000). Consequently, it is a challenging task for modelers to provide59
information that is practically usable and interpretable by a broader community of end users (Fatichi et al., 2016).60
Ideally, HMs would be accessible to any potential users so that they are able to test different decisions and sce-61
narios themselves. Potential users who are not hydrology experts can include data analysts, decision-makers, and also62
scientists in other disciplines who aim to incorporate water-related topics into their models. In situations where dif-63
ferent disciplines need to work closely together, and where models from different areas such as economics, hydrology,64
climatology or ecology may need to be integrated, further obstacles often emerge, as HMs often need to be designed,65
exchanged and run by different user groups. Moreover, several models with overlapping features may be available, and66
selecting an appropriate model for a task can be challenging even for experienced modelers (Surfleet et al., 2012). In67
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addition, enabling different capabilities of a model can lead to different data and input requirements.68
Even for hydrology experts, it can be difficult to understand how processes are represented in different HMs, making69
comparison studies very time-consuming. HMs tend to have special computational requirements and use heterogenous70
file formats for spatio-temporal data, so that data pre-processing usually requires basic programming skills. Additional71
technical challenges arise when HMs require different operating systems or complex model configurations, which can72
limit the applicability and transferability of models even for hydrology experts. Therefore, there is a great need for73
new approaches to facilitate the dissemination of HMs to users who lack the expertise to develop them but are invested74
in using them for decision-making purposes.75
Over the last few decades, efforts have been made to make HMs more accessible by integrating them into Geo-76
graphic Information Systems (GIS) (Bittner et al., 2020; Rossetto et al., 2018; Refsgaard et al., 2010). In this regard,77
GIS-based interfaces to HMs often act as an essential component of a Decision Support System (DSS) (Lautenbach78
et al., 2009; Pezij et al., 2019; Zhang et al., 2014).79
Executable and well-structured DSSs make HMs even applicable by non-expert groups, but DSSs usually lack80
transferability as they are strongly tailored to the individual conditions of a defined case study. An example of how81
DSSs are often developed in the course of a project to combine different stand-alone software tools can be found in82
(Kinzelbach et al., 2021). However, a limitation of many DSS is that they are desktop-based and therefore show limited83
accessibility. Moreover, they often focus on one area, such as groundwater and even on one model and are thus lacking84
interoperability. GIS-based interfaces have been used in the Soil Water Assessment Tool (SWAT) (Arnold et al.,85
1998), the Free and open source software tools WATer resources management system (FREEWAT) (Koltsida and86
Kallioras, 2019) or the Hydrologic Engineering Center - Hydrologic Modeling System (HEC-HMS) (US Army Corps87
of Engineers, 2000), but models must be set up from scratch by experienced users. Furthermore, these platforms only88
include a single HM, while users often want to use several HMs to compare their results. Consequently, initiatives like89
the Community Surface Dynamics Modeling System (CSDMS) (Peckham and Syvitski, 2007; Peckham et al., 2013),90
the Earth System Modeling Framework (ESMF) (Hill et al., 2004) or the HydroShare platform (Horsburgh et al., 2016)91
have already taken a step forward to provide and combine multiple models from different disciplines. CSDMS and92
ESMF include the dissemination of final and calibrated models combined with their results from a variety of disciplines93
in the field of Geo- and Earth Science (Overeem et al., 2013; Collins et al., 2005; Keller et al., 2014), while HydroShare94
is explicitly designed for the exchange, storage or management of hydrological datasets and models (Gan et al., 2020).95
However, these efforts are focused on users who are modeling experts pursuing science research, rather than non-expert96
users.97
In order to ease the dissemination of expert models to non-experts, our previous work introduced the Model IN-98
Tegration Framework (MINT) (Gil et al., 2018, 2021). MINT defined the components and interfaces needed to assist99
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expert modelers when setting up pre-existing HMs for non-experts. But adding new HMs to the framework required100
advanced software engineering skills, making it challenging for expert users to contribute. This paper builds on our101
previous work, with the following novel contributions:102
1. A methodology that follows principles of software engineering to create software components for HMs with a103
simple invocation function with pre-set inputs and parameters, capturing metadata about the model that can be104
used to provide guidance to non-expert users.105
2. An implementation of this methodology that guides expert modelers to create model components, integrated in106
the Model INTegration Framework (MINT) (Gil et al., 2018, 2021).107
3. Two use cases that demonstrate the use of this methodology and implementation for two models that differ in108
terms of hydrological processes they consider, as well as in terms of their individual code structure: SWAT109
(Arnold et al., 1998) and MODFLOW (Harbaugh, 2005).110
This methodology makes models more findable, accessible, interoperable, and reusable in support of FAIR prin-111
ciples (Wilkinson et al., 2016).112
The paper begins with a description of our proposed methodology for creating software components for models113
(Section 2). Next, in Section 4, we illustrate how the methodology is implemented in the MINT Model Insertion114
Checker, a standalone application designed to guide users through the proposed methodology steps. Section 5 describes115
two examples that follow our methodology to deliver two different HM configurations for two different regions of the116
globe. Section 6 shows how each of these configurations can be accessed and run in the MINT platform. Section 7117
discusses the main advantages and limitations of our approach, and Section 8 presents conclusions and future work.118
2. Background119
HMs differ in the way they conceptualize the characteristics and flow processes in a natural system. As a result,120
HMs usually have dozens of parameters and input files which vary across different scenarios. For example, models121
like SWAT may use an input file with snowmelt observations in regions with mountains but may not take snowmelt122
into consideration if there are no mountains around the basin of interest. Expert hydrologists, who we will refer to here123
as modelers, need to make decisions about which hydrological processes and corresponding parameters are relevant124
to the intended non-expert users (e.g., decision-makers, analysts, researchers with expertise in other areas or domains,125
students in training or citizens who are active in non-governmental organization), who we will refer to as users.126
2.1. Software Components127
Encapsulating software into portable components allows other users to easily run software on their own machine128
without worrying about the environment and set up needed (Boettiger, 2015; Kurtzer et al., 2017) Following well-129
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established component-based software engineering principles, we aim to create self-contained software components130
that only reveal functionality that is of interest to third-party users. This is important because scientific software com-131
ponents are often implemented in large packages or libraries that can be used for various steps such as data preparation132
and visualization in addition to writing software to simulate specific processes (such as atmospheric dynamics for133
climate models, runoff and infiltration for hydrology models, fuel density for fire modeling, etc.).134
Software packages can be quite overwhelming for users, even when they are familiar with the scientific domain for135
which the package was written. Usability becomes even more challenging for users outside of the domain, although136
these users are precisely the ones who may benefit the most from the results of the respective packages.137
Existing graphical user interfaces (GUIs) and GIS systems are often often difficult to reuse from other programs.138
User interfaces usually have a specific function to call the software with a button, using a form which users operate139
to define specific parameters (typically the most relevant ones). That function call (sometimes called a command140
line invocation) is reusable from different programs, provided that the software tool can be run from a machine with141
its specific execution environment. The function call uses inputs that can be provided when invoking the software142
component (as it is done in a user interface where the values for some input parameters are set). Other inputs can be143
pre-set within the component (including data files) if there are no reasons for third party users to change them given a144
specific use case.145
A software component corresponds to a single invocation function for software. Given a sophisticated software146
package with multiple purposes, a software component may be created to include only certain processes and variables,147
a specific pre-processing step, or a specific visualization. For example, a hydrology model software may be pre-set to148
be applicable to hot arid regions only and ignore the processes (and therefore inputs) describing snowmelt.149
2.2. Model Configurations & Encapsulation150
We use the term model encapsulation to refer to the process of creating easy-to-use self-standing executable soft-151
ware components from models for a target scenario. We refer to these software components as model configurations.152
Expert modelers are responsible for designing these model configurations for a region by identifying the key parame-153
ters that non-experts should be able to modify. Model configurations declare only those relevant parameters or input154
files that users should be able to change so the model configuration can be easily set up and run to explore different155
scenarios.156
The remainder of this section illustrates model configurations through a simple example, introduces key concepts157
in model encapsulation, and describes the main steps of our methodology.158
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Figure 1: Overview of two model configurations. The first configuration (left) exposes snow
(01.sno) and topographic input files (01.sub) associated with spring melt. Two specific
parameters of the 01.sno file are exposed, namely snowmelt temperature and maximum
melt factor, as well as the simulation time as general boundary conditions (stored in
file.cio). Additional exposed files include output.rch file storing streamflow, as well as the
output.sub file to make snowmelt accessible. The second configuration (right) focuses on
a smaller basin inside the country, and it is limited to discharge. The simulation time and
the maximum melt factor are pre-set with a meaningful default value.
2.3. Model Configurations: An Example159
For illustration purposes, let us consider Alice, an expert hydrology modeler, and Bob, a decision-maker with160
little hydrological expertise. Bob needs to regulate policies for the water budget at a country scale, and therefore he161
is interested in obtaining a rough estimate on water availability during the Spring season. In particular, Bob seeks to162
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understand: 1) whether the water demand of specific crops can be met under different assumptions and 2) the impact163
of runoff for energy production, i.e., from hydro-electrical plants. Given her expertise, Bob asks Alice to provide an164
environment where he can run model simulations according to his requirements.165
Alice anticipates that Bob may want to modify some of the simulation parameters affecting snowmelt, the dom-166
inant runoff component and source of water in Spring. A shift in the onset and duration of snowmelt usually affects167
the temporal water availability of the agricultural and energy sector. Alice decides to use the SWAT model and creates168
two model configurations to predict streamflow as a proxy representation for water availability. The first model con-169
figuration is designed at the country level, letting Bob modify the snowmelt temperature and the maximum possible170
snowmelt to explore the effects on agriculture (e.g., what crop yield can be achieved by different crops). The second171
model configuration focuses on a small basin located in the Northeastern region of the country, in order to study the172
conditions and effects of snowmelt for a potential small hydroelectric power plant. Both model configurations first173
undergo a strict and rigorous calibration and validation procedure by Alice, a necessary expert step to ensure a reliable174
baseline for the further usage. The calibration and validation serve as fundamental steps to provide robust and credible175
models.176
Figure 1 shows an overview of the model configurations prepared by Alice, with the country-level configuration on177
the left and the basin configuration on the right. Each configuration has one or multiple inputs and outputs, representing178
the files accepted and produced by a configuration. We use the term parameters to refer to values a user may be179
interested in changing in a model, such as snowmelt temperature, even if these values are declared within configuration180
files. We consider as parameters hydrological or process-based variables, together with temporal information such as181
simulation length or time step, here referred to as boundary conditions (BC). A code wrapper captures how to invoke a182
model configuration by indicating how the command line should be invoked, and specifies any fixed values of inputs.183
When creating a model configuration, a modeler like Alice may have to choose which of the inputs or parameters184
should be adjustable by the final user, among the dozens or hundreds of input files and parameters HMs have. We use the185
term expose to indicate that a parameter or input file can be adjustable by a user in a model configuration. For example,186
SWAT contains hundreds of files, but Alice estimates that the relevant ones for the country-level configuration are two187
input files with snow and elevation information. As shown in Figure 1, the input file containing snow information188
further includes the parameters that will be exposed to users, namely snowmelt temperature and the maximum melt189
factor of snow. Adapting the threshold temperature when snow begins to melt is an easy way to shift the melt season190
within the country. The second parameter provides information on the amount of snowmelt one could expect. Alice191
thus provides a meaningful range of values, within which Bob is able to increase or decrease the amount of snowmelt.192
In addition, Alice decides to expose a file that includes general information on BC like the time for which the model193
was set up or its temporal resolution, daily in this case. Thanks to this information, Alice expects Bob to be able to194
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Table 1
Overview of terminology used that has a specific definition in that paper, but might have
an ambiguous use outside our work.
Term Description
Model encapsulation Process of creating easy-to-use and independent software components (e.g. from a
model)
Model configuration Abstracted version of a model which considers only relevant inputs, outputs and
parameters that are adjustable. Model configurations represent software components
Boundary condition General information of a model such as temporal information
Parameter Hydrological or process-based variable where users might be interested in to change
Expose Indicates that a specific parameter or file is adjustable by the user
Expert Hydrology expert used to modelling
User A non-expert in the field of modelling, such as for example citizens, decision makers,
researchers from other fields, analysts
Wrapper Captures how to invoke a model configuration and specifies fixed values
compare the effects of a very high and a very low value for snowmelt temperature as well as the maximum melt factor195
on the water availability.196
As for the basin configuration, Alice is familiar with the area from her previous work. Therefore, she decides to197
set up all default values of the model according to her knowledge of the region. She exposes snowmelt temperature198
by making only this parameter available in the basin configuration. This configuration is more restricted, but more199
precisely tailored to the region at hand. Therefore, this model configuration is simplified by allowing Bob to only200
modify snowmelt temperature. Hence, Bob can now obtain alternative estimates with respect to the accumulation of201
snow during winter, which is then available as melt water. This enables the decision-maker to infer whether a small202
hydropower plant might be of value or not or how much energy could be produced under various snowfall conditions.203
In summary, with these model configurations the modeling expert is able to hide the complexity of a general204
model exposing only what is relevant for a country and its hydrology, narrowing it down to a much more usable model205
component for other users to explore scenarios and make decisions accordingly. It should also be mentioned that Bob206
doesn’t necessarily must be a decision maker. However, he could also be an interested member of a NGO which deals207
with environmental issues for example or just an interested citizen increasingly affected by hydrological events such208
as drought or heavy rain.209
Table 1 provides an overview about the terminology we use in this paper, especially to distinguish terms which might210
ambiguous and are used differently in other fields.211
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Table 2
Overview of the main steps of our proposed model encapsulation methodology.
Description Result
Step 1: Start a New Environment Modeler indicates a working folder (it
may be empty)
System prepares a basic execution en-
vironment (e.g., Unix and Python)
The system populates the component folder
structure, including a setup file containing
information on the target model component
and creating an empty software container
Step 2: Trace Execution Dependencies Modeler runs a test execution
System detects dependencies to exe-
cute the model and adds them to the
container
Container that includes execution
dependencies for the model run
Step 3: Expose Parameters Modeler indicates user-adjustable pa-
rameters to be exposed
Modeler specifies default values
System stores parameter exposure
and links to configuration files
File containing parameter information
Step 4a: Expose Input Files Modeler indicates input file types ex-
pected by the configuration
File containing the input file selection
Step 4b: Expose Output Files Modeler indicates output file types
produced by the configuration
File containing the output file selection
Step 5: Create Wrapper Script Modeler reviews the execution shell
script created by the system to run
the new model component according
to the specified settings, and does a
test run of the component
System ensures that the test run com-
pletes successfully, and uses the pro-
vided input/output description, pa-
rameter settings and shell script to
create the model component as a con-
tainer with the required dependencies.
Creation of subfolders and files with
encapsulation and execution information
Step 6: Model Upload System uploads the model component Registration of model component in
container and code repositories and model
catalog
3. A Methodology for Model Encapsulation212
We propose a methodology for creating model configurations. Our methodology requires expert modelers to de-213
termine the main parameters and input files that need to be exposed for a given executable model, including steps for214
guiding and testing the final model configuration so other users can use it effectively. Our methodology comprises six215
main steps: Start a New Environment, Trace Execution Dependencies, Expose Parameters, Expose Input & Output216
Files, Wrap Execution, and Model Upload. Table 2 provides a summary of all steps, which are further described here.217
Step 1: Start a New Environment: Modelers start by specifying the location for the folder structure of the new218
model component they want to create. This should be started in a “clean" computing environment, free from other219
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software dependencies installed on the local machine. For example, if a model is available in Python, starting in a220
clean environment makes it easy to isolate the model needs from other Python libraries installed in the machine for221
other purposes. This can be achieved by using virtual environments, that create a clean Python installation with no222
installed package dependencies. In our methodology we adopt software containers, a common approach to capture223
computational environments. Software containers enable capturing the dependencies of a software component at the224
operating system level (i.e., including not only the dependencies of a software component, but all the system depen-225
dencies as well), hence ensuring that it can be run in other environments.226
Because containers can be complicated to set up and use for non-computer scientists, our system will be automati-227
cally creating the container and installing the dependencies and files needed to run the model. The modeler can see228
everything that the system is adding in the folder that they specified.229
Step 2: Trace Execution Dependencies and Run Model: Once an environment has been set up, the dependencies230
needed to install the model must be incorporated into the environment. This includes compilers, system libraries, and231
other files. The modeler carries out a test run that is representative of how the model configuration will be used. During232
the run, the system automatically detects the model input, configuration, and output files used by the model during the233
run. This information is added to the container environment and used by the system in subsequent steps in order to234
assist the modeler to specify inputs and outputs.235
Figure 2: An illustration of how the snowmelt temperature parameter (SMTMP) of a
SWAT model is exposed so it is accessible for users to adjust for different scenarios: a)
shows the original .bsn file with the default value assigned to SMTMP, b) shows how
the default value is exposed so it can be changed by a user when running the model
configuration.
Step 3: Expose Model Parameters and Define Configuration Files. Most HMs have dozens of parameters and BCs236
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which specify constants like hydraulic conductivity, bulk density, or the general settings of the simulation. Within237
this step, modelers have to define which of these parameters and BCs they want to expose to users in the new model238
configuration. For example, the CN2 (Curve Number II) parameter of SWAT is usually one of the parameters which239
is typically changed during the model setup and calibration (the process of estimating relevant parameters and their240
corresponding values) and might be a useful parameter to expose in a model configuration.241
HMs usually adjust numerical values for their simulations in two different ways: 1) with the invocation command used242
to run the model; or 2) through configuration files that can be edited directly or accessed via user interfaces. If a file243
is used, it needs to be specified by the modeler. Fig. 2 illustrates this with an example of how the snowmelt base244
temperature parameter is exposed for the SWAT hydrology model (SMTMP) through a configuration file.245
Step 4: Expose Model Inputs and Outputs. Next, modelers have to decide which input and output files they want to246
expose, which depends on the intended use cases that users will want to simulate. As with parameters and BCs, expert247
modelers usually provide the relevant input files required by a model. Likewise, models produce all sorts of output248
variables, and for a given configuration only a certain subset of outputs may be relevant for the intended use cases. For249
instance, a modeler may expose only output files containing drought-related variables such as evapotranspiration and250
soil moisture.251
Step 5: Create a Wrapper Script. Once the parameters, BCs and files to be exposed have been specified, the next252
step is to write a shell script which captures how to run the model configuration. We refer to this script as the wrapper253
script, as it wraps the model configuration as an executable component. The wrapper script will make sure that the254
component can run with the inputs and outputs selected by the modeler, and may include pre-set files or values for other255
inputs and parameters. In order to verify that the model works appropriately with the wrapper script, it is necessary256
for the modeler to provide sample input files which are used in a test run. If everything works successfully, the model257
configuration is completed and will be executable in other computational environments.258
Step 6: Upload the model configuration. The final step is to deposit the model configuration in shared repositories.259
First, the script and test data used to wrap up the model configuration should be deposited in a code repository. Second,260
an archival version of the model software code must be created in a code repository, to ensure that that version can261
always be accessed by users in the future. Third, the container environment should be uploaded to a container registry.262
Finally, the model configuration should be uploaded to a model catalog, with proper model configuration metadata263
provided by the modeler to enable discovery and reuse.264
4. Methodology Implementation265
We implemented our methodology in the MINT Model Insertion Checker (MIC), a standalone application devel-266
oped to guide users through the process of creating new model configurations. MIC performs all the steps of our267
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Figure 3: Overview of the methodology steps as well as the resources created by each
step.
methodology in a semi-automated manner, integrating the results with commonly used software and container image268
repositories such as GitHub and DockerHub. MIC also integrates new model configurations and the metadata in the269
MINT modeling framework and its model catalog (Garijo et al., 2019). MIC is implemented as a Unix-based tool that270
runs in the command line, and is available as open source software (Osorio et al., 2022).
1
A step by step tutorial is271
1
Software respository is at https://github.com/mintproject/mic/ with documentation at (https://mic-cli. readthedocs.io/e
n/latest/overview/)
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available online
2
to help users and disseminate the steps of our methodology.272
Figure 3 provides an overview on how MIC implements all the steps of our methodology, capturing the main273
software dependencies, input, parameters and generated files, and showing how the methodology steps are related274
to one another. MIC guides users through the six steps outlined in our methodology. It starts with a blank Unix275
environment, generated with a basic Docker image, where users are asked to install and run their model from scratch276
(step 1). Once a sample run is finished, MIC tracks which files have been used and generated using ReproZip (Rampin277
et al., 2016), an application designed to trace all dependencies and system calls of a program (step 2). Using the278
output from ReproZip, MIC drafts an initial component, based on the inputs and outputs detected in the test run. Next,279
MIC works with the modeler to get information about the inputs, parameters and BCs of HMs should be exposed280
in the model configuration, among all the candidates detected automatically (steps 3 and 4). The preparation of the281
configuration file is one of the few activities that has to be carried out manually by the modeler, as it involves information282
highly dependent on the use cases required by the intended users. For example, SWAT may be used to create multiple283
configurations depending on whether modelers need to expose snowmelt temperature, hydraulic conductivity or a factor284
to delay groundwater flow. The parameters and BCs exposed with MIC will be adjustable by users when running the285
model configuration. If one of the exposed parameters or BCs are stored in a configuration file, an additional step is286
required to indicate where to replace the target value in that file. An example can be seen in Figure 2, where snowmelt287
temperature in SWAT is exposed through the SMTMP parameter which can be provided by users at runtime. All the288
information provided to MIC is stored in a MIC settings file that can be inspected and edited by modelers at any time,289
e.g., to change default values for parameters or to make adjustments on what is exposed to users.290
Once all the inputs, outputs, parameters and BCs form a specific model configuration are set, MIC will prompt291
users to perform a test run using all default values. MIC automatically creates an execution wrapper script (step 5) and292
runs the model using the local environment created earlier in the second step. If successful, the model configuration293
is ready to be run by others, and MIC will prompt users to double check if the results from the execution are correct.294
As a final step, MIC saves the model configuration (step 6) including:295
1. the computational environment used in the test run, saved as a Docker image in DockerHub
3
296
2. the wrapper script and settings file containing the exposed inputs, outputs, parameters and BCs. MIC will297
store these files in a new GitHub repository, owned by the modeler who created the model configuration298
3. basic metadata about the model configuration, including its main title, description, version of the model, geo-299
graphic location, execution details and brief parameter and input descriptions. These metadata are submitted to300
the MINT model catalog, producing the results shown in Figure 4.301
2
https://mic-cli.readthedocs.io/en/latest/model_configuration/03a-step1/
3
https://hub.docker.com/
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Figure 4: An example of how a model configuration is used by modelers and users: a) the
modeler provides all the metadata, including the parameters and files exposed to the user,
and specify default values for some of the parameters so end users only need to adjust one
of them (snowfall temperature); b) end users wanting to use that model component can
specify different values of the parameters and submit model runs that correspond to the
scenarios they want to explore.
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In the following sections we provide several screenshots of MIC to familiarize the reader and potential users with the302
platform. Figure 4 shows an example of how the model configuration can be accessed by a user after being created with303
MIC. Figure 4 a) depicts a model configuration where four parameters are exposed (i.e., minimum and maximum melt304
factors, snowfall temperature and snowmelt temperature) out of the dozens of parameters that are available in SWAT.305
Figure 4 b) shows an example where only one of the four parameters (snowfall temperature) may be changed by users306
when running a second, different configuration of SWAT (the other three parameters are fixed). Both configurations307
of the model are integrated in the MINT framework, where they can be executed through a GUI.308
5. Creating Model Components: Two Practical Use Cases309
In this section we showcase our methodology by encapsulating two different and widely used hydrological models,310
i.e, SWAT and MODFLOW, using MIC to create model components and running them in the MINT platform. By311
pointing out the specific differences of SWAT and MODFLOW, we illustrate the main concepts of our methodology312
as well as the technical features of MIC that facilitate model dissemination for any type of HM. We show model313
configurations for SWAT and MODFLOW for two different case studies. Each case study was defined prior to our314
work by a different research group working with stakeholders in different regions of the world.315
5.1. SWAT: Background and Model Structure316
The Soil Water Assessment Tool (SWAT) is a semi-distributed, time-continuous model developed by the Blackland317
Research & Extension Center of the United States Department for Agriculture (USDA) (Arnold et al., 1998). SWAT318
is based on the concept of the Hydrologic Response Units (HRU) and was originally developed to assess the impact of319
land management practices in large watersheds, while the applications nowadays range from water quality or sediment320
transport studies up to snow-hydrological in basins all over the world Arnold and Fohrer (2005).321
HRUs are the smallest spatial unit within the model and defined on the subbasin scale, a further subdivision of the322
watershed. However, HRUs are not spatially located and are formed by unique combinations of land use, soil and323
slope within each subbasin to consider spatial heterogeneity. The HM is organized by input files grouped by different324
processes or characteristics, such as land management or soil inputs, for the individual spatial units. Besides, the325
model includes few general files where basic settings can be done. SWAT separates its calculations in a land and a326
water phase. It first calculates all loadings for the HRUs in each subbasin, which are then transferred to the stream. In327
a second step the in-stream processes, covering routing processes as well as chemical processes, are calculated.328
5.2. MODFLOW: Background and Model Structure329
The MODular Finite-difference FLOW model (MODFLOW), is a fully-distributed and physically-based ground-330
water model, developed by the United States Geological Survey (Harbaugh, 2005; Hanson et al., 2014). MODFLOW331
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is organized in modules, which allow for user customization of specific case studies (i.e., by selecting only those332
modules that are relevant). For instance, a module can represent different solvers for the groundwater flow equation.333
Moreover, various modules exist to account for different hydrological processes in a natural system, e.g., stream flow,334
evapotranspiration or groundwater recharge. Given the grid-based nature of the model, several modules can be cou-335
pled by providing grid coordinates in the input files. If specific modules should be used in a model run, an input file is336
required for each respective module. These input files are ASCII files, either organized in a table format or grid-based.337
All modules to be used for a model simulation have to be included in a configuration file, i.e., a name (.nam) file.338
Depending on the interaction of different hydrological processes, MODFLOW solves the groundwater flow equation339
and provides water budgets for each pre-defined discrete time step in an output file, the list (.lst) file.340
5.3. Model Implementation341
In the following we describe how our methodology, described in section 3, is implemented for two different HMs,342
namely SWAT and MODFLOW. Most of the steps are similar for both models (and to other HMs), despite how different343
their software and approach are. Therefore, we focus on demonstrating how users can describe the models following344
our methodology using different use cases.345
5.3.1. Case Studies346
The location of our study areas and their geographical characteristics are illustrated in Figure 5. Our case studies347
focus on two very distinct hydrological systems: the Naryn River in Kyrgyzstan for SWAT and the Barton Springs348
segment of the Edwards aquifer in Texas for MODFLOW. For each case study we emphasize which part of the proposed349
methodology is similar and where differences occur, which mainly concern the exposed inputs and outputs in the350
respective model configurations. The BCs, such as simulation period and time step, have been set by experts for both351
case studies. The target user groups of both cases are non-expert analysts and decision-makers. Our intention is to352
grant the respective users access to the model configurations, so that they are able to run alternative scenarios on their353
own. A summary of the case studies can be found in Table 3.354
5.3.2. Snow dynamics in the Naryn Basin - Case Study355
Our first case study focuses on a part of the Naryn Basin located in Kyrgyzstan, where high flow occurs mainly356
in Spring and Summer due to snow and glacier melt. In contrast, low flow phases are mostly restricted to the winter357
season. The basin belongs to one of the headwater streams of the Syr Darya, one of the two major tributaries of the358
Aral Sea and drains an area of around 50,000 km². Our case study focuses on the headwaters of the basin, which359
originate in the Tianshan mountains. Snow and glacier melt are of great concern for the local population, as it provides360
water for energy and agriculture (Unger-Shayesteh et al., 2013; Gan et al., 2015). The parameters exposed concern361
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Figure 5: Regions in the two case studies: on the left the Barton Springs Segment of the
Edwards Aquifer in Texas for MODFLOW, on the right the Naryn Basin in Kyrgyzstan for
SWAT.
snowmelt and snowfall (a full list is provided in Table 3). The choice is based on preliminary investigations that362
comprised a comprehensive sensitivity analysis and calibration of our SWAT model (Schaffhauser et al., 2023). These363
parameters (snowfall temperature, snowmelt temperature, maximum and minimum melt rate) proved to be among364
the most sensitive ones providing a reasonable model performance. The case study represents an example where the365
model is intended to be used by local authorities. Our configuration provides an example of an abstraction that can366
be used by both non-experts and more experienced users. In this case, the non-experts will be decision-makers in367
an agricultural agency, while the more advanced users will be in the local water authority who will have a broader368
expertise in water-related questions. The model component shall finally be used by these decision-makers to examine369
the effects of changes in snow processes on streamflow. Snow processes constitute the dominant source of water and370
serves as a proxy of water availability in spring for the region. An exploration of the timing and amount of snowmelt371
provides decision-makers with valuable insights on the available water for different sectors, such as agriculture or372
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Table 3
Summary of the two case studies, describing the main characteristics of the two case
studies. For case study 2, the abbreviations M-A, M-B and M-I refer to the three model
components based on average conditions with default pumping rates, a baseline consider-
ing drought conditions and a component where the user can specify pumping rates and
infiltration. Exposed parameters and input files indicate those elements highlighted by the
expert modeler for each scenario which can be customized by others.
Case Study 1 Case Study 2
Hydrological Model SWAT MODFLOW
Name of Model Configuration Naryn - SWAT Barton Springs - MODFLOW
Region Naryn Basin, Kyrgyzstan Edwards Aquifer, Texas, US
Region Size 10,000 km² 401 km²
Scenario Summary Water resources management, floods, crop
yield, energy production
Sustainable yield, drought assessment,
evaluation of pumping rates under stress
conditions
Dominant Processes Snow and glacier related Infiltration, pumping
Exposed Parameters Snowmelt temperature, snowfall temperature,
max. melt factor summer, min. melt factor
winter
None
Exposed Input files basin.bsn Baseline model (M-B) & model with average
conditions (M-A): None, Infiltration model
(M-I): infiltration, pumping rates & recharge,
wells
Exposed Output Variables Streamflow Hydraulic head, total water storage, total
volume extracted
energy.373
This information is important in many aspects. For example, authorities can deduce how much water is expected374
to be available for agriculture. This enables an estimate of the expected yield within the crop season, one of the major375
economic factors for the region. In addition, this water is required to be stored for energy production of the whole376
country. Besides, the period is prone to floods, frequently causing at least local threats. By having model outputs377
of water availability, decision-makers can allocate water to different purposes, advance or delay planting dates, and378
generally prepare for the specific seasonal requirements such as energy or irrigation demand.379
Accordingly, we share the model configuration to enable these users to adjust the snow-related parameters, namely380
snowmelt temperature, snow fall temperature as well as the minimum and maximum snowmelt factors. Users can381
then explore their own scenarios, and monitor the actual conditions of the basin to assess which of their scenarios382
correspond to the actual conditions of the current season.383
To provide some initial scenarios, we provide a set of default values for all snow parameters to provide users with a384
starting point. As the response variable of interest for the end-user is discharge, only the corresponding output file385
is exposed in our component. For simplicity, we decided to predefine all input files so that users cannot make any386
changes.387
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5.3.3. Drought impact on the water budget in Barton Springs - Case Study388
The second study refers to the Edwards Aquifer and more precisely the Barton Springs segment in Austin, Texas,389
a region increasingly affected by droughts (Passarello et al., 2012, 2014). A numerical simulation, using the MOD-390
FLOW model, was developed for use as a groundwater availability model (GAM) in the state of Texas (Scanlon et al.,391
2001, 2003).392
The MODFLOW configuration was prepared as part of a state-wide planning activity. The components underwent393
a scientific vetting process to assess groundwater availability. The intended end users are groundwater managers for394
state-designated management districts, as well as stakeholders involved in the recurring groundwater aquifer man-395
agement program of the state of Texas. They are not hydrology experts necessarily, although they have expertise in396
groundwater. Water availability fluctuates rapidly in the region, due to normal variability in weather and climate con-397
ditions. As urban areas have expanded in the past decade, water consumption has increased and habitats for vulnerable398
species are at greater risk for impact during dry conditions. Table 3) shows an overview of the models. We created a399
model component M-B that reflects a baseline model for drought conditions with default pumping rates. We created400
a separate component M-A for average conditions, also with default pumping rates. M-B was explicitly designed to401
investigate and emphasize potential adverse effects of pumping under dry conditions. In contrast, M-A shows the im-402
pacts of similar pumping conditions under normal non-drought conditions. We also created a third component M-I403
where the user can specify infiltrated water (as a recharge input file) and pumping rates (as a wells input file). The404
components are designed to expose key model outputs concerning water table levels (hds output file representing hy-405
draulic head levels), storage (cbb output file representing volumes), and actual pumping rates (cbb output file).406
The recharge zones were developed for Barton Springs GAM because it represents a baseline interpretation of ground-407
water behavior, the model is readily accessible. The recharge zones were originally completed as part of a Groundwater408
Decision Support System developed to assess the sustainable yield (Pierce et al., 2006; Pierce, 2006).409
5.3.4. Model Encapsulation410
The following subsections demonstrate the model encapsulation of each case study. A summarized overview of411
the steps and the differences in the procedure (where users have to perform manual adaptions) for each case study, is412
shown in Table 4. The encapsulation process follows the model preparation steps (usually including calibration and413
validation) which are performed by the expert modeler.414
5.3.4.1. STEP 1: Start New Environment An environment has to be created for each model configuration (see415
Section 2.3). For case study 1, the modeler would create a single model component focused on the snow processes416
of SWAT. For case study 2, the modeler chose to create three separate model components: one for baseline drought417
conditions, one for baseline average conditions, and a third one for analyzing different scenarios in average conditions.418
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Table 4
Overview of the steps conducted in MINT for the dissemination of the two case studies.
We highlight where users have to incorporate manual modifications and which explicit
setting we made in our example.
SWAT MODFLOW
1) Start New environment no difference except name of the model
configuration
no difference except name of the model
configuration
2) Trace Execution Dependencies execution command is model-specific
./swat670
execution command is model-specific ./mf6
3) Expose Parameters MIC command (mic pkg parameters),
parameters are model and case specific,
here: snowfall temperature, snowmelt
temperature, maximum & minimum melt
rate
MIC command (mic pkg parameters),
parameters are model and case specific, here:
none adjustable parameter defined
4a) Expose Input Files MIC command (mic pkg inputs), desired
input files to share are model and case
specific, in this case basins.bsn
MIC command (mic pkg inputs), desired
input files to share are model and case specific
4b) Expose Output Files MIC command (mic pkg outputs), desired
output files to share are model and case
specific, in this case reach.rch
MIC command (mic pkg outputs), desired
output files to share are model and case
specific, in this case .hds, .lst
5) Create Wrapper Script MIC command (mic pkg wrapper, mic pkg
run), manual & model-specific adaptions
when default parameter changes are
desired
MIC command (mic pkg wrapper, mic pkg
run), manual & model-specific adaptions
when default parameter changes are desired
6) Model Upload MIC command (mic pkg upload),
automatically uploads the model
configuration to DockerHub, GitHub and
MINT
MIC command (mic pkg upload),
automatically uploads the model
configuration to DockerHub, GitHub and
MINT Model Catalog
The modeler starts MIC from the command line, where he provides the name of the model configuration. In our case,419
the names are Naryn - SWAT and Barton Springs - MODFLOW 1 to 3. MIC automatically creates the folder structure420
for each model configuration.421
5.3.4.2. STEP 2: Trace Execution Dependencies The modeler then does a test run to check if the respective422
model is installed in a new environment and to trace the execution dependencies. Then, MIC is used to trace input and423
output dependencies (through ReproZip). Since MIC is a Unix-based tool, the invocation command for SWAT refers424
to the Unix-based execution file, which can be downloaded via the SWAT homepage.
4
As for MODFLOW, we used425
the Python-based FloPy tool for the model encapsulation.
5
FloPy serves as a tool which is used to execute existing426
MODFLOW-based models.427
5.3.4.3. STEPS 3 & 4: Expose Parameters, Inputs and Outputs For the SWAT model configuration, several428
snow parameters were exposed, which were snowfall temperature, snowmelt temperature and the maximum and min-429
4
https://swat.tamu.edu/software/swat-executables/
5
https://www.usgs.gov/software/flopy-python-package-creating-running-and-post-processing-modflow-b ased-mo
dels
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imum melt factors. The parameter selection was based on a preliminary study done by the modeler with relevant430
stakeholders to identify the dominant parameters (see also Table 3). Each parameter exposed must be manually spec-431
ified in MIC, as described in Section 4. Subsequently, the parameters must be indicated in the corresponding SWAT432
input files (as shown in Figure 2). Adjustments of default parameter values are possible during this step as well. Next,433
the modeler declares the input files that contain the exposed parameters as configuration files. Since all snow parame-434
ters of SWAT are stored in the basin file (basin.bsn), it is the only configuration file relevant to the model configuration.435
The users in the Naryn case study, such as authorities related to the agricultural, energy or water sector, do not need436
all the output files so only the output.rch file is exposed, as it contains all required information on streamflow within437
the basin.438
For the configurations of the MODFLOW model in case study 2 no parameters were exposed. For the drought model439
component only the .hds and .lst input files were exposed, where the relevant information of the hydraulic head and440
the water budget can be specified by users.441
5.3.4.4. STEP 5: Create Wrapper Script MIC helps wrap model configurations by taking into account the ex-442
ecution settings and prepares the files to test the model components. The test runs done by MIC were based on the443
default parameter settings defined in the previous step and double-checked manually. After the test run, the model444
configuration was finalized and ready for upload.445
5.3.4.5. STEP 6: Model Upload Finally, MIC uploads the model configurations to relevant repositories. The446
Docker image of the model component was uploaded to DockerHub.
6 7
A GitHub repository containing the input447
data and results was also created
8
. Finally, an entry in the MINT model catalog was created,
9 10
and the model can be448
easily run from the MINT user interface.449
6. Scenario Exploration by Non-Expert Users with New Model Configurations450
This section describes how users can access the newly created model configurations of the two case studies. It451
highlights how users can easily specify simulation scenarios using the model configurations.452
6.1. Accessing Model Components453
Users can browse all model configurations, for example by bringing up the corresponding regions, Kyrgyzstan454
and Texas, or browsing entries in the MINT model catalog. Typically, a user starts in the “Use Models" tab, and455
6
https://hub.docker.com/r/mosorio/naryn_nival_setup/tags
7
https://hub.docker.com/r/mintproject/modflow-2005/tags
8
Components are archived in Zenodo: https://zenodo.org/record/6948339.Yue0VHZByMq
9
https://mint.isi.edu/kyrgyzstan/models/explore/SWAT/8cc84426-d849-471b-9a5e -4 7bcaf094607/6a36a2e5-73bf-4
098-9acd-1aaaab383d4a/14580635-c7ca-4256-935a-4ddbdacfbfe2
10
htt ps:/ /mint.is i.ed u/te xas/mode ls/e xplore/M ODFL OW/m odflow_20 05/m odflow_20 05_cf g/modflo w_20 05_Ba rtonSpri n
gs_avg
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Figure 6: Illustration how the two case studies can be accessed within the MINT modeling
framework, each shown on the left and right sides of the figure. The upper panels refer
to the selection of the study area from a map. The lower panels show the corresponding
problem statements that drive the set up and execution of the model configurations.
specifies a problem statement by selecting a time period for the simulation, a region of interest, and desired response456
variables (i.e., simulation outputs). Once the problem statement is specified, MINT will show the user relevant model457
configurations that can be run. Fig. 6 shows the MINT user interface to access model configurations in the different458
regions. More details are provided in the next section.459
6.2. Model, Dataset & Parameter Selection460
The Naryn case study aims to simulate discharge by adjusting the snow parameters that govern the predominant461
processes in the region. In detail, these processes involve snowmelt and snowfall and therefore the snowpack distribu-462
tion in the region. These processes control discharge generation. Thus, a task was created where river discharge was463
used as response variable. As shown in Figure 7 a), it would also be possible to use other models to obtain discharge,464
such as TopoFlow (Peckham, 2009). A similar overview for the Barton Springs case study is provided in Figure 8.465
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Figure 7: Illustration of: a) available model components for the simulation of discharge
for the Naryn case study; b) the snow parameter modification of our model within the
problem statement and task section of MINT, exemplified at the Naryn case study. It is
demonstrated how the four exposed snow parameters are predefined with default values,
that can be directly adjusted here.
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Figure 8: Illustration of: a) available model components for the drought assessment of the
Barton Spring case study. b) the parameter modification of our model within the problem
statement and task section of MINT, again exemplified at the case study of the Barton
Springs. Due to the static design, no parameters can be adjusted, but the model can
simply be run with the default parameter values.
Multiple model components may be selected, which would allow users to easily create a model ensemble to provide466
a more differentiated picture. At this point the user would choose among all available input datasets, such as mete-467
orological information with precipitation and temperature. An example can be found in Appendix A1 where users468
can choose between two alternative well files (with different pumping rates) for the Barton Springs area, which would469
allow them to evaluate the effects on the groundwater levels under various pumping rates.470
Users can also easily specify different parameter values to reflect different scenarios or assumptions. As described in471
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Section 5.3.2, four snow parameters were exposed and the user can assign them different values to explore different472
scenarios, as shown in Figure 7 b). This allows users to explore different dates for the onset of the snowmelt season,473
represented by various temperature thresholds. If users provide several values, MINT runs the model multiple times.474
All outputs are provided individually for each run. In the parameter tab (Figure 7 b) users can also check the default475
values of each parameter. This is particularly relevant if the default values are based on expert knowledge and refer to476
a specific baseline which can be used for the comparison with developed scenarios. The static design of the Barton477
Springs components does not allow for any parameter changes, thus the corresponding tab is empty (see Figure 8 b).478
6.3. Execute & Analyze Model Components479
When users set parameter values, the model component can be executed and the “Results" tab in MINT provides a480
summary of all the outputs. In the Naryn use case, the outputs were limited to the output.rch file where the discharge481
information is stored. The results represent the discharge response to the adjusted snow dynamics in the basin, which482
were compared with the baseline simulation results obtained with the default parameter values (no shift). For every483
parameter combination a model run was executed and the corresponding outputs were generated. An example is shown484
in Appendix A2, where the user specified two values for each of the four parameters which led to 16 combinations485
each resulting in an execution and each with its output files. To eliminate unnecessary computations, MINT caches486
the results of executions.487
MINT can generate some standard visualizations, but users often generate their own custom visualizations after
Figure 9: Results of the discharge simulation with the SWAT case study when the scenario is run (green line), compared
to the default setup (black line). The figure indicates the seasonal shift towards an early melt onset under changing
conditions. The example intends to show what future versions of MINT could directly visualize. The lines represent a
mean over five years for each Julian Day.
488
downloading the results in a post-processing step. We show a visualization of the results from the SWAT case study489
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comparing a particular scenario versus the baseline in Figure 9. Users can now directly derive the desired information,490
depending on how strong the average shift in river discharge would be under changing snow conditions. The results491
shown in the figure represent the mean of a five-year period. From the plot it becomes apparent that the conditions492
represented by the scenario would lead to a strong rise in discharge in early March already. Besides, the earlier onset493
and steep rise of snowmelt would cause higher discharge in April and May compared to the baseline, while it would be494
reduced during the summer months. The annual peak would already occur 1.5-2 months earlier than in the baseline.495
This significant change in the flow regime may have far-reaching consequences for the water sector. For example, the496
summer reduction may affect agricultural production as irrigation water is missing, while the strong increase after the497
winter months may promote the damage potential of flood events. Ultimately, water authorities may conclude to assess498
the potential of a reservoir to mitigate those undesired effects.499
For the second case study, we declared a problem statement where we included the different Barton Springs model500
components. To access the model configurations, the study area in MINT has first to be changed to Texas (analogously501
to Fig. 6), where we can then select Barton Springs - MODFLOW. Users can create tasks, which reflect a specific502
scenario, and select an appropriate model configuration. One of the scenarios may focus on drought assessment,503
using the M-B configuration. Another task may be for average conditions, using the M-A configuration. A third task504
may focus on the impacts of specific recharge and pumping conditions, using the M-I configuration. Users have the505
possibility to compare the three different setups and easily analyze the differences in groundwater availability in the506
region. In detail, one can evaluate the effects of pumping on groundwater levels and study how the aquifer should507
be managed to maintain flow under specific conditions. Users might infer that, under drought conditions, pumping508
alone is not sufficient. In contrast to the SWAT model configuration, where only one output file is accessible, the509
MODFLOW model configurations offer four different output files.510
It is worth noting that the application of the scenarios does not require any computing/programming skills for users.511
However, if users want to run encapsulated models locally, basic container skills are required. In general this is seldom512
the case, since MINT relies on user-friendly GUI (Fig. 6 and 8).513
7. Discussion514
Models created by experts are usually difficult to use by modelers in other disciplines. Despite the need by decision-515
makers to access sophisticated models, they remain inaccessible to non-experts (Bagstad et al., 2013). Even experts516
within a discipline find that it takes significant effort to setup and compare models from other modelers (Lüke and517
Hack, 2018; Francesconi et al., 2016). Our work shows that two very different hydrological models could be encapsu-518
lated using the same methodology to simplify model dissemination by experts for use by non-experts. Our MIC tool519
can be used by expert modelers without major knowledge of software engineering (e.g., using software containers,520
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managing execution dependencies, or setting up code repositories). We demonstrated the methodology for different521
model domains, purposes, technical details, and model structures.522
Our case studies illustrate that modelers only have to determine the parameters and input and output files to be523
exposed, according to the intended scenarios. Different uses of a model (e.g., snow-related analysis or studies focusing524
on crop yield) lead to different model configurations and are organized and easily accessible in MINT. The methodology525
enables expert modelers to create useful abstractions of existing models. The abstraction hides the part of the model526
complexity that is not necessarily required for the target users. Therefore, once a model has been encapsulated with527
our methodology, non-expert users are relieved from dealing with the technical details of the model execution or its528
structure.529
Different types of non-experts may benefit from our effort, depending on their expertise and background. For530
example, citizens of hydrological extremes (drought and floods), who become relevant stakeholders and develop a531
certain level of expertise to understand their own scenarios; NGO members who are interested in model applications532
in the environmental sector; or decision makers who usually have a decent hydrological know-how, but may not be533
familiar with modeling (water authorities are often busy with administrative work, which means that there is little time534
for the construction and calibration of complex models). Additionally, we envision expert modelers to benefit from535
this effort, as it facilitates the creation of model ensembles for model comparisons or for benchmarking.536
Our methodology may be used to share and use pre-agreed scenarios (as in our Barton Springs case study), and537
support users developing their own scenarios independently by modifying the exposed parameters. We also included538
the possibility of exposing input datasets in model configurations so users can select their own. For example, several539
meteorological data sets may be used for the execution of a model configuration. Processing all required input data540
is time-consuming and HMs often have different requirements. Exchanging these data often represents an obstacle541
(Gardner et al., 2018) that can be at least partially overcome by using MINT. Modelers are also encouraged to describe542
their configurations with metadata so that users can search flexibly for models and use those that are suitable for their543
scenarios. A region-specific search (which corresponds to Kyrgyzstan or Texas in our examples) allows users finding544
all available models for that region. Modelers should also provide code for output visualizations (see Section 6.3).545
The integration of a general visualization environment in MINT would facilitate the usability in extended scenarios,546
for example by integrating other datasets that may be relevant to the modeling scenarios (e.g., population density, road547
access, etc.).548
Although the examples of this paper focus on hydrological models, our methodology has been applied to models549
in other domains, including agriculture and economics. We assume all encapsulated models to be open source, or have550
an open source executable that can be shared in a software container.551
This methodology helps aligning a software component with the findable, accessible, interoperable, and reusable552
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principles (FAIR) for data (Wilkinson et al., 2016), following current best practices for Open Science. By creating553
software components that have specific functionality and clear invocation and results, modelers provide self-contained554
and pre-prepared model components that are well characterized and become easier to reuse than the original modeling555
software. Model components are more accessible than the original modeling software as they are encapsulated in556
a software container that can be executed in any platform. Model components include extensive machine-readable557
software metadata that makes them more findable and interoperable.558
Finally, it is worth noting that we used pre-calibrated models for our case studies. Future work will address this559
limitation by integrating model calibration capabilities into our framework and methodology.560
8. Conclusions561
This paper introduced a methodology to simplify the dissemination of expert models to non-expert users. The562
methodology guides modeling experts when creating software components that explore specific modeling scenarios.563
The methodology is applicable to any kind of model, regardless of its discipline, processes or technical details. The564
implementation of the methodology in the MIC tool enables a simple model encapsulation process for modelers.565
This does not only facilitate model dissemination and provision, but can also improve mutual work within or across566
disciplines and groups. In addition, the complexity of the model can be simplified by creating model configurations that567
suit the needs of non-expert users. Our proposed methodology thus creates new possibilities in model abstractions and568
promotes the satisfaction of end-user needs. This is also supported by the easy access options of model configurations569
in MINT, which greatly simplifies their (re)use.570
We illustrated our methodology with two case studies, using two different hydrological models in two different re-571
gions of the world. The case studies provide examples how potential scenarios and use cases for the application of the572
methodology could look like. However, the universal applicability of the methodology within any modeling discipline573
enables a free design of scenarios with numerous potential use cases that can help both, the expert modeler as well574
as the end-user. MINT users can easily compare the effects of pumpage under different conditions on groundwater575
levels. Moreover, they can infer whether pumping is suitable to maintain flow under drought conditions or if additional576
measures should be taken into account. Additionally, we showed how a restriction of the parameter space to a useful577
minimum can facilitate the exploration of discharge shifts by decision-makers. The methodology encourages the pos-578
sibility of independently investigating scenarios and to derive valuable insights. For example, resulting discharge shifts579
may lead to several consequences for the water sector, e.g., increased flood risk or decreased agricultural production580
to mention only two out of dozens, that call for action.581
Our work supports the FAIR principles, helping model components to be more findable, accessible, interoperable582
and reusable. However, our methodology also presents some limitations, which are part of our future work. For583
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example, while our methodology helps non-experts executing models created by expert modelers, some expertise is584
still needed to interpret the results of the simulations. In some cases this is addressed by adding documentation and585
metadata in the scenario, in order to provide the right context for end users. In other cases, expert modelers include ad-586
hoc visualizations that are executed with the model itself, helping to interpret the outputs. Extending our methodology587
to ensure that visualization components are described for each model output would help address this issue. We are also588
exploring extending MINT with general-purpose visualizations (e.g., variables obtained in tabular model results).589
Another point of improvement involves expanding the supported actions for modeling experts in MINT. For ex-590
ample, including additional data transformations and model calibration (right now models are calibrated by experts591
independently).592
Finally, additional case studies in other domains are part of our future work in order to further refine the applicability593
of our approach when disseminating models across disciplines, lowering the barrier of adoption of models by modeling594
experts.595
Software and Data Availability596
Name of the software: Model component 1 - Snow dynamics597
Developer: Timo Schaffhauser (t.schaffhauser@tum.de), Maximiliano Osorio (mosorio@isi.edu)598
Software availability: https://hub.docker.com/r/mosorio/naryn_nival_setup/tags (Docker image)599
Compressed size: 286.97 MB (Docker image)600
601
Name of the software: Model component 2 - Drought impact602
Developer: Suzanne Pierce (spierce@tacc.utexas.edu), Maximiliano Osorio (mosorio@isi.edu)603
Software availability: https://hub.docker.com/r/mintproject/modflow-2005/tags (Docker image)604
Compressed size: 733.55 MB (Docker image)605
606
Name of the software: Model Insertion Checker (MIC)607
Developer: Maximiliano Osorio (mosorio@isi.edu)608
Software availability: https://zenodo.org/record/6024985}.YvPflnZByMo/609
Programming language: Python610
Compressed size: 19.9 MB611
612
Name of the dataset: SWAT & MODFLOW Model Components613
Developer: Timo Schaffhauser (t.schaffhauser@tum.de), Daniel Garijo, Maximiliano Osorio, Daniel Bittner, Suzanne614
First Author et al.: Preprint submitted to Elsevier Page 30 of 37
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Pierce, Hernan Vargas, Markus Disse, Yolanda Gil615
Data availability: https://zenodo.org/record/6948339}.YvJ6V3ZByMr616
Form of repository: Zenodo archive617
Compressed size: 51.7 MB618
619
Further access to the model components is possible via https://mint.isi.edu/kyrgyzstan/models/explo620
re/SWAT/8cc84426-d849-471b-9a5e-47b caf094607/6a 36a2e5-73bf-4098-9acd-1aaaab383d4a/145 806621
35-c7 ca-4256-935a-4ddbdacfbfe2 and https://mint.isi. edu/t exas/models/explore/MODFLOW/modf622
low_2005/modflow_2005_cfg/modflow_2005_BartonSprings_avg.623
Acknowledgements624
We would like to thank our collaborators in the MINT project, particularly modeling experts and users that con-625
tributed to the design of the overall MINT framework. We gratefully acknowledge support from the US Defense Ad-626
vanced Research Projects Agency through award W911NF-18-1-0027 and the US Office of Naval Research through627
award N00014-21-1-2437. The authors from TUM also want to thank the BMBF (Bundesministerium für Bildung628
und Forschung) for the funding of the "OekoFlussPlan" project (grant number 01LZ1802B). The author from UPM629
has been supported by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with630
Universidad Politécnica de Madrid in the line Support for R&D projects for Beatriz Galindo researchers, in the context631
of the V PRICIT (Regional Programme of Research and Technological Innovation) and the call Research Grants for632
Young Investigators from Universidad Politécnica de Madrid.633
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Appendices634
A1635
Figure A1: Illustration of the selection of different input datasets for the well files. For the Barton Springs case study users
would have two possibilities in that case.
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A2636
Figure A2: Example of: a) in total 16 different runs of the model component, since for all exposed parameters two different
values were set; and b) the corresponding 16 output files, which were generated through running the component with all
16 potential parameter value combinations.
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